This year (2015) people living in South Minneapolis have filed a record-amount of complaints of aircraft noise. Each complaint requires a phone call. As an engineer working in the IoT industry, I believe the residents of any neighborhood near an Airport could benefit from a small, cheap solution that can identify when an aircraft flies over their house and files a complaint for them.

What it does

Powered by a microprocessor with a Python interpreter, an audio input and an internet connection, the device will continuously sample the Sound Pressure Level (SPL) in decibels as well as analyze the timbre of the sampled audio to determine if the sound heard was a commercial aircraft. The device will continuously report the SPL to Exosite, but will make its best effort to report an aircraft event when its native algorithm makes an appropriate determination.

The reason for the analysis of the audio timbre processing on the device is to keep a large portion of the signal processing performed on the gear the user/community has already paid for. It makes no sense to do processing in the cloud when a cheap device is capable of it as well. This will keep the cost of the IoT solution down.

MAC currently has 39 listening towers. From this picture one can easily see there are underserved neighborhoods. The aim of this project is to crowd-source the datacollection and provide another means of accountability for airport noise. In engineering, it is good practice to have someone besides the developer test the code / hardware. It's a system of checks and balances just like the American system of government. Currently, MAC seems to monitor, police and regulate itself. With a democratic monitoring system, we might find that some neighborhoods' complaints will now be backed by evidence.

Other useful links

How I built it

Current plan is to do a raspberry pi build with a cheap microphone - perhaps a simple peizo element will suffice since we're mostly just after SPL and some rough frequency information.

Challenges I ran into

Accomplishments that I'm proud of

What I learned

What's next for Neighborhood Airport Noise Monintor

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posted an update

We did it! We completed a Numpy component on the Raspberry Pi that records audio and compares it against a library of aircraft sounds, an Exosite pipeline to take the noise events and send them to the web interface, and a web interface to view the aggregated data. What an awesome Saturday!

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